def test_isoup_tree_model_description(): stream = RegressionGenerator(n_samples=700, n_features=20, n_informative=15, random_state=1, n_targets=3) stream.prepare_for_use() learner = iSOUPTreeRegressor(leaf_prediction='mean') max_samples = 700 X, y = stream.next_sample(max_samples) # Trying to predict without fitting learner.predict(X[0]) learner.partial_fit(X, y) expected_description = "if Attribute 11 <= 0.36737233297880056:\n" \ " Leaf = Statistics {0: 450.0000, 1: [-23322.8079, -30257.1616, -18740.9462], " \ "2: [22242706.1751, 29895648.2424, 18855571.7943]}\n" \ "if Attribute 11 > 0.36737233297880056:\n" \ " Leaf = Statistics {0: 250.0000, 1: [33354.8675, 32390.6094, 22886.4176], " \ "2: [15429435.6709, 17908472.4289, 10709746.1079]}\n" \ assert SequenceMatcher(None, expected_description, learner.get_model_description()).ratio() > 0.9
def test_evaluate_multi_target_regression_coverage(tmpdir): from skmultiflow.data import RegressionGenerator from skmultiflow.trees import MultiTargetRegressionHoeffdingTree max_samples = 1000 # Stream stream = RegressionGenerator(n_samples=max_samples, n_features=20, n_informative=15, random_state=1, n_targets=7) stream.prepare_for_use() # Learner mtrht = MultiTargetRegressionHoeffdingTree(leaf_prediction='adaptive') output_file = os.path.join(str(tmpdir), "prequential_summary.csv") metrics = [ 'average_mean_square_error', 'average_mean_absolute_error', 'average_root_mean_square_error' ] evaluator = EvaluatePrequential(max_samples=max_samples, metrics=metrics, output_file=output_file) evaluator.evaluate(stream=stream, model=mtrht, model_names=['MTRHT'])
def demo(output_file=None): """ Test iSOUP-Tree This demo demonstrates how to evaluate a iSOUP-Tree multi-target regressor. Parameters ---------- output_file: string The name of the csv output file """ stream = RegressionGenerator(n_samples=5000, n_features=20, n_informative=15, random_state=1, n_targets=7) stream.prepare_for_use() regressor = iSOUPTreeRegressor(leaf_prediction='adaptive') # Setup the evaluator evaluator = EvaluatePrequential(pretrain_size=1, batch_size=1, n_wait=200, max_time=1000, output_file=output_file, show_plot=False, metrics=[ 'average_mean_square_error', 'average_mean_absolute_error', 'average_root_mean_square_error' ]) # Evaluate evaluator.evaluate(stream=stream, model=regressor)
def test_hoeffding_tree(): stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1) stream.prepare_for_use() learner = HoeffdingAdaptiveTreeRegressor(leaf_prediction='mean', random_state=1) cnt = 0 max_samples = 500 y_pred = array('d') y_true = array('d') wait_samples = 10 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_true.append(y[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('d', [ 102.38946041769101, 55.6584574987656, 5.746076599168373, 17.11797209372667, 2.566888222752787, 9.188247802192826, 17.87894804676911, 15.940629626883966, 8.981172175448485, 13.152624115190092, 11.106058099429399, 6.473195313058236, 4.723621479590173, 13.825568609556493, 8.698873073880696, 1.6452441811010252, 5.123496188584294, 6.34387187194982, 5.9977733790395105, 6.874251577667707, 4.605348088338317, 8.20112636572672, 9.032631648758098, 4.428189978974459, 4.249801041367518, 9.983272668044492, 12.859518508979734, 11.741395774380285, 11.230028410261868, 9.126921979081521, 9.132146661688296, 7.750655625124709, 6.445145118245414, 5.760928671876355, 4.041291302080659, 3.591837600560529, 0.7640424010500604, 0.1738639840537784, 2.2068337802212286, -81.05302946841077, 96.17757415335177, -77.35894903819677, 95.85568683733698, 99.1981674250886, 99.89327888035015, 101.66673013734784, -79.1904234513751, -80.42952143783687, 100.63954789983896 ]) assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 143.11351404083086 assert np.isclose(error, expected_error) expected_info = "HoeffdingAdaptiveTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='mean', " \ "learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \ "max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \ "nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \ "stop_mem_management=False, tie_threshold=0.05)" info = " ".join([line.strip() for line in learner.get_info().split()]) assert info == expected_info assert isinstance(learner.get_model_description(), type('')) assert type(learner.predict(X)) == np.ndarray
def demo(input_file, output_file=None): """ _test_mtr_regression This demo demonstrates how to evaluate a Multi-Target Regressor. The employed dataset is 'scm1d', which is contained in the data folder. Parameters ---------- input_file: string A string describind the path for the input dataset output_file: string The name of the csv output file """ stream = RegressionGenerator(n_samples=5000, n_features=20, n_informative=15, random_state=1, n_targets=7) stream.prepare_for_use() classifier = MultiTargetRegressionHoeffdingTree(leaf_prediction='adaptive') # Setup the pipeline pipe = Pipeline([('Classifier', classifier)]) # Setup the evaluator evaluator = EvaluatePrequential(pretrain_size=1, batch_size=1, n_wait=200, max_time=1000, output_file=output_file, show_plot=False, metrics=['average_mean_square_error', 'average_mean_absolute_error', 'average_root_mean_square_error']) # Evaluate evaluator.evaluate(stream=stream, model=pipe)
def test_hoeffding_tree_regressor_perceptron(): stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1) stream.prepare_for_use() learner = HoeffdingTreeRegressor(leaf_prediction='perceptron', random_state=1) cnt = 0 max_samples = 500 y_pred = array('d') y_true = array('d') wait_samples = 10 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_true.append(y[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('d', [ 1198.4326121743168, 456.36607750881586, 927.9912160545144, 1160.4797981899128, 506.50541829176535, -687.8187227095925, -677.8120094065415, 231.14888704761225, -284.46324039942937, -255.69195985557175, 47.58787439365423, -135.22494016284043, -10.351457437330152, 164.95903200643997, 360.72854984472383, 193.30633911830088, -64.23638301570358, 587.9771578214296, 649.8395655757931, 481.01214222804026, 305.4402728117724, 266.2096493865043, -445.11447171009775, -567.5748694154349, -68.70070048021438, -446.79910655850153, -115.892348067663, -98.26862866231015, 71.04707905920286, -10.239274802165584, 18.748731569441812, 4.971217265129857, 172.2223575990573, -655.2864976783711, -129.69921313686626, -114.01187375876822, -405.66166686550963, -215.1264381928009, -345.91020370426247, -80.49330468453074, 108.78958382083302, 134.95267043280126, -398.5273538477553, -157.1784910649728, 219.72541225645654, -100.91598162899217, 80.9768574308987, -296.8856956382453, 251.9332271253148 ]) assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 362.98595964244623 assert np.isclose(error, expected_error) expected_info = "HoeffdingTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='perceptron', " \ "learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \ "max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \ "nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \ "stop_mem_management=False, tie_threshold=0.05)" info = " ".join([line.strip() for line in learner.get_info().split()]) assert info == expected_info assert isinstance(learner.get_model_description(), type('')) assert type(learner.predict(X)) == np.ndarray
def test_hoeffding_tree_regressor_perceptron(): stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1) stream.prepare_for_use() learner = HoeffdingTreeRegressor(leaf_prediction='perceptron', random_state=1) cnt = 0 max_samples = 500 y_pred = array('d') y_true = array('d') wait_samples = 10 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_true.append(y[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('d', [525.7553636732247, 352.8160300365902, 224.80744320456478, 193.72837054292074, 132.6059603765031, 117.06974933197759, 114.53342429855932, 89.37195405567235, 57.85335051891305, 60.00883955911155, 47.263185779784266, 25.17616431074491, 17.43259526890146, 47.33468996498019, 22.83975208548138, -7.659282840823236, 8.564101665071064, 14.61585289361161, 11.560941733770441, 13.70120291865976, 1.1938438210799651, 19.01970713481836, 21.23459424444584, -5.667473522309328, -5.203149619381393, 28.726275200889173, 41.03406433337882, 27.950322712127267, 21.267116786963925, 5.53344652490152, 6.753264259267268, -2.3288137435962213, -10.492766334689875, -11.19641058176631, -20.134685945295644, -19.36581990084085, -38.26894947177957, -34.90246284430353, -11.019543212232008, -22.016714766708127, -18.710456277443544, -20.5568019328217, -2.636583876625667, 24.787714491718187, 29.325261678088406, 45.31267371823666, -48.271054430207776, -59.7649172085901, 48.22724814037523]) # assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 152.12931270533377 assert np.isclose(error, expected_error) expected_info = "HoeffdingTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='perceptron', " \ "learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \ "max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \ "nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \ "stop_mem_management=False, tie_threshold=0.05)" info = " ".join([line.strip() for line in learner.get_info().split()]) assert info == expected_info assert isinstance(learner.get_model_description(), type('')) assert type(learner.predict(X)) == np.ndarray
def test_stacked_single_target_hoeffding_tree_regressor_adaptive(test_path): stream = RegressionGenerator(n_samples=2000, n_features=20, n_informative=15, random_state=1, n_targets=3) stream.prepare_for_use() learner = StackedSingleTargetHoeffdingTreeRegressor( leaf_prediction='adaptive', random_state=1) cnt = 0 max_samples = 2000 wait_samples = 200 y_pred = np.zeros((int(max_samples / wait_samples), 3)) y_true = np.zeros((int(max_samples / wait_samples), 3)) while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred[int(cnt / wait_samples), :] = learner.predict(X) y_true[int(cnt / wait_samples), :] = y learner.partial_fit(X, y) cnt += 1 test_file = os.path.join( test_path, 'expected_preds_stacked_single_target_hoeffding_tree_adaptive.npy') expected_predictions = np.load(test_file) assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 150.7836894811965 assert np.isclose(error, expected_error) expected_info = "StackedSingleTargetHoeffdingTreeRegressor(binary_split=False, grace_period=200,\n" \ " leaf_prediction='adaptive',\n" \ " learning_ratio_const=True,\n" \ " learning_ratio_decay=0.001,\n" \ " learning_ratio_perceptron=0.02,\n" \ " max_byte_size=33554432,\n" \ " memory_estimate_period=1000000,\n" \ " nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=None,\n" \ " random_state=1,\n" \ " remove_poor_atts=False,\n" \ " split_confidence=1e-07,\n" \ " stop_mem_management=False,\n" \ " tie_threshold=0.05)" assert learner.get_info() == expected_info assert isinstance(learner.get_model_description(), type(''))
def test_multi_output_learner_regressor(): stream = RegressionGenerator(n_samples=5500, n_features=10, n_informative=20, n_targets=2, random_state=1) stream.prepare_for_use() estimator = SGDRegressor(random_state=112, tol=1e-3, max_iter=10, loss='squared_loss') learner = MultiOutputLearner(base_estimator=estimator) X, y = stream.next_sample(150) learner.partial_fit(X, y) cnt = 0 max_samples = 5000 predictions = [] true_targets = [] wait_samples = 100 correct_predictions = 0 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): predictions.append(learner.predict(X)[0]) true_targets.append(y[0]) if np.array_equal(y[0], predictions[-1]): correct_predictions += 1 learner.partial_fit(X, y) cnt += 1 expected_performance = 2.444365309339395 performance = mean_absolute_error(true_targets, predictions) assert np.isclose(performance, expected_performance) assert learner._estimator_type == "regressor" assert type(learner.predict(X)) == np.ndarray with pytest.raises(AttributeError): learner.predict_proba(X)
def test_multi_target_regression_hoeffding_tree_mean(test_path): stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1, n_targets=3) stream.prepare_for_use() learner = MultiTargetRegressionHoeffdingTree(leaf_prediction='mean') cnt = 0 max_samples = 500 wait_samples = 10 y_pred = np.zeros((int(max_samples / wait_samples), 3)) y_true = np.zeros((int(max_samples / wait_samples), 3)) while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred[int(cnt / wait_samples), :] = learner.predict(X) y_true[int(cnt / wait_samples), :] = y learner.partial_fit(X, y) cnt += 1 test_file = os.path.join( test_path, 'expected_preds_multi_target_regression_mean.npy') expected_predictions = np.load(test_file) # print(expected_predictions.shape) assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 167.40626294018753 assert np.isclose(error, expected_error) expected_info = \ 'MultiTargetRegressionHoeffdingTree: max_byte_size: 33554432 - ' \ 'memory_estimate_period: 1000000 - grace_period: 200 - ' \ 'split_criterion: intra cluster variance reduction - ' \ 'split_confidence: 1e-07 - tie_threshold: 0.05 - binary_split: False' \ ' - stop_mem_management: False - remove_poor_atts: False ' \ '- no_pre_prune: False - leaf_prediction: mean - nb_threshold: 0 - ' \ 'nominal_attributes: [] - ' assert learner.get_info() == expected_info assert isinstance(learner.get_model_description(), type(''))
def test_isoup_tree_mean(test_path): stream = RegressionGenerator(n_samples=2000, n_features=20, n_informative=15, random_state=1, n_targets=3) stream.prepare_for_use() learner = iSOUPTreeRegressor(leaf_prediction='mean') cnt = 0 max_samples = 2000 wait_samples = 200 y_pred = np.zeros((int(max_samples / wait_samples), 3)) y_true = np.zeros((int(max_samples / wait_samples), 3)) while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred[int(cnt / wait_samples), :] = learner.predict(X) y_true[int(cnt / wait_samples), :] = y learner.partial_fit(X, y) cnt += 1 test_file = os.path.join( test_path, 'expected_preds_multi_target_regression_mean.npy') expected_predictions = np.load(test_file) assert np.allclose(y_pred, expected_predictions) error = mean_absolute_error(y_true, y_pred) expected_error = 191.2823924547882 assert np.isclose(error, expected_error) expected_info = "iSOUPTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='mean', " \ "learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \ "max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \ "nominal_attributes=None, random_state=None, remove_poor_atts=False, split_confidence=1e-07, " \ "stop_mem_management=False, tie_threshold=0.05)" info = " ".join([line.strip() for line in learner.get_info().split()]) assert info == expected_info assert type(learner.predict(X)) == np.ndarray
def test_regression_hoeffding_tree_model_description(): stream = RegressionGenerator( n_samples=500, n_features=20, n_informative=15, random_state=1 ) stream.prepare_for_use() learner = RegressionHoeffdingTree(leaf_prediction='mean') max_samples = 500 X, y = stream.next_sample(max_samples) learner.partial_fit(X, y) expected_description = "if Attribute 6 <= 0.1394515530995348:\n" \ " Leaf = Statistics {0: 276.0000, 1: -21537.4157, 2: 11399392.2187}\n" \ "if Attribute 6 > 0.1394515530995348:\n" \ " Leaf = Statistics {0: 224.0000, 1: 22964.8868, 2: 10433581.2534}\n" assert SequenceMatcher( None, expected_description, learner.get_model_description() ).ratio() > 0.9
def test_evaluate_regression_coverage(tmpdir): # A simple coverage test. Tests for metrics are placed in the corresponding test module. from skmultiflow.data import RegressionGenerator from skmultiflow.trees import RegressionHoeffdingTree max_samples = 1000 # Stream stream = RegressionGenerator(n_samples=max_samples) stream.prepare_for_use() # Learner htr = RegressionHoeffdingTree() output_file = os.path.join(str(tmpdir), "prequential_summary.csv") metrics = ['mean_square_error', 'mean_absolute_error'] evaluator = EvaluatePrequential(max_samples=max_samples, metrics=metrics, output_file=output_file) evaluator.evaluate(stream=stream, model=htr, model_names=['HTR'])